CLAIM-MS is a method for finding functionally related genes. The novelty
of this proposition is in its flexibility, as the method integrates
information from many input data sources of different types. We
successfully validated it on gene expression data produced by
different technologies (microarray, RNA-seq) and experiment setups
(case-control or multi-class, single-time-point or time-series), on
protein-protein interaction networks and Gene Ontology annotations. For
each dataset, a gene-gene distance metric needs to be derived in
accordance with its nature and the experiment setup.

This approach expands our previous work with, among others:

the ability to handle more than two data sources at once;

a new robustly converging clustering algorithm (a neural gas method);

a more efficient clique detection algorithm;

deep analysis of underlying distance matrices, which allow tuning up
the evaluation of gene clusters with respect to a particular biological
dataset; this procedure significantly improves the overall quality of
the outcomes.